Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations2988650
Missing cells1876559
Missing cells (%)4.8%
Duplicate rows37207
Duplicate rows (%)1.2%
Total size in memory925.2 MiB
Average record size in memory324.6 B

Variable types

DateTime1
Numeric7
Categorical4
Text1

Alerts

Dataset has 37207 (1.2%) duplicate rowsDuplicates
brand is highly overall correlated with cat1 and 1 other fieldsHigh correlation
cat1 is highly overall correlated with brand and 1 other fieldsHigh correlation
cat2 is highly overall correlated with brand and 1 other fieldsHigh correlation
cust_request_tn is highly overall correlated with customer_id and 2 other fieldsHigh correlation
customer_id is highly overall correlated with cust_request_tn and 1 other fieldsHigh correlation
product_id is highly overall correlated with cust_request_tn and 2 other fieldsHigh correlation
sku_size is highly overall correlated with product_idHigh correlation
tn is highly overall correlated with cust_request_tn and 2 other fieldsHigh correlation
plan_precios_cuidados is highly imbalanced (91.0%) Imbalance
stock_final has 1839319 (61.5%) missing values Missing
cust_request_tn is highly skewed (γ1 = 37.70988076) Skewed
tn is highly skewed (γ1 = 37.87580231) Skewed
stock_final has 34082 (1.1%) zeros Zeros

Reproduction

Analysis started2025-05-19 03:00:46.825437
Analysis finished2025-05-19 03:03:15.286250
Duration2 minutes and 28.46 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
Minimum2017-01-01 00:00:00
Maximum2019-12-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-19T00:03:15.452690image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:03:15.654001image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

customer_id
Real number (ℝ)

High correlation 

Distinct597
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10171.395
Minimum10001
Maximum10637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2025-05-19T00:03:15.781315image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10007
Q110053
median10133
Q310267
95-th percentile10447
Maximum10637
Range636
Interquartile range (IQR)214

Descriptive statistics

Standard deviation142.03964
Coefficient of variation (CV)0.013964617
Kurtosis-0.22512637
Mean10171.395
Median Absolute Deviation (MAD)98
Skewness0.81392422
Sum3.0398741 × 1010
Variance20175.26
MonotonicityNot monotonic
2025-05-19T00:03:15.938718image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 25122
 
0.8%
10004 24487
 
0.8%
10003 24100
 
0.8%
10002 23553
 
0.8%
10007 23469
 
0.8%
10018 22645
 
0.8%
10027 22634
 
0.8%
10059 21983
 
0.7%
10005 21518
 
0.7%
10034 19705
 
0.7%
Other values (587) 2759434
92.3%
ValueCountFrequency (%)
10001 25122
0.8%
10002 23553
0.8%
10003 24100
0.8%
10004 24487
0.8%
10005 21518
0.7%
10006 18419
0.6%
10007 23469
0.8%
10008 9192
 
0.3%
10009 16749
0.6%
10010 11464
0.4%
ValueCountFrequency (%)
10637 2
 
< 0.1%
10636 5
 
< 0.1%
10635 51
 
< 0.1%
10634 16
 
< 0.1%
10633 2
 
< 0.1%
10632 2
 
< 0.1%
10631 21
 
< 0.1%
10630 65
 
< 0.1%
10629 8
 
< 0.1%
10626 187
< 0.1%

product_id
Real number (ℝ)

High correlation 

Distinct1233
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20423.189
Minimum20001
Maximum21299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2025-05-19T00:03:16.087164image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile20023
Q120155
median20360
Q320650
95-th percentile21014
Maximum21299
Range1298
Interquartile range (IQR)495

Descriptive statistics

Standard deviation312.94155
Coefficient of variation (CV)0.015322854
Kurtosis-0.6238578
Mean20423.189
Median Absolute Deviation (MAD)237
Skewness0.59229109
Sum6.1037765 × 1010
Variance97932.413
MonotonicityNot monotonic
2025-05-19T00:03:16.245322image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20037 9996
 
0.3%
20100 9812
 
0.3%
20020 9706
 
0.3%
20230 9360
 
0.3%
20010 9222
 
0.3%
20021 8782
 
0.3%
20105 8204
 
0.3%
20022 8008
 
0.3%
20111 7973
 
0.3%
20122 7950
 
0.3%
Other values (1223) 2899637
97.0%
ValueCountFrequency (%)
20001 6172
0.2%
20002 6000
0.2%
20003 6793
0.2%
20004 7139
0.2%
20005 5911
0.2%
20006 6497
0.2%
20007 6906
0.2%
20008 6453
0.2%
20009 5596
0.2%
20010 9222
0.3%
ValueCountFrequency (%)
21299 1
< 0.1%
21298 1
< 0.1%
21297 1
< 0.1%
21296 1
< 0.1%
21295 1
< 0.1%
21294 1
< 0.1%
21293 1
< 0.1%
21292 1
< 0.1%
21291 1
< 0.1%
21290 2
< 0.1%

plan_precios_cuidados
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size165.3 MiB
0
2954621 
1
 
34029

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2988650
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2954621
98.9%
1 34029
 
1.1%

Length

2025-05-19T00:03:16.413337image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-19T00:03:16.585151image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2954621
98.9%
1 34029
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 2954621
98.9%
1 34029
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2988650
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2954621
98.9%
1 34029
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2988650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2954621
98.9%
1 34029
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2988650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2954621
98.9%
1 34029
 
1.1%

cust_request_qty
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1495019
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2025-05-19T00:03:16.716083image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum92
Range91
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5805763
Coefficient of variation (CV)1.6657702
Kurtosis54.039778
Mean2.1495019
Median Absolute Deviation (MAD)0
Skewness6.3281174
Sum6424109
Variance12.820527
MonotonicityNot monotonic
2025-05-19T00:03:16.877762image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2066593
69.1%
2 451619
 
15.1%
3 152223
 
5.1%
4 83170
 
2.8%
5 47223
 
1.6%
6 32179
 
1.1%
7 23616
 
0.8%
8 18792
 
0.6%
9 14345
 
0.5%
10 11975
 
0.4%
Other values (74) 86915
 
2.9%
ValueCountFrequency (%)
1 2066593
69.1%
2 451619
 
15.1%
3 152223
 
5.1%
4 83170
 
2.8%
5 47223
 
1.6%
6 32179
 
1.1%
7 23616
 
0.8%
8 18792
 
0.6%
9 14345
 
0.5%
10 11975
 
0.4%
ValueCountFrequency (%)
92 1
< 0.1%
90 1
< 0.1%
88 1
< 0.1%
85 2
< 0.1%
84 1
< 0.1%
83 1
< 0.1%
79 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%

cust_request_tn
Real number (ℝ)

High correlation  Skewed 

Distinct101954
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47691053
Minimum0.0001
Maximum551.56137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2025-05-19T00:03:17.076281image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.00209
Q10.01057
median0.04095
Q30.1638
95-th percentile1.604051
Maximum551.56137
Range551.56127
Interquartile range (IQR)0.15323

Descriptive statistics

Standard deviation3.276818
Coefficient of variation (CV)6.8709283
Kurtosis2789.3242
Mean0.47691053
Median Absolute Deviation (MAD)0.03658
Skewness37.709881
Sum1425318.6
Variance10.737536
MonotonicityNot monotonic
2025-05-19T00:03:17.309737image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01638 19921
 
0.7%
0.04095 16229
 
0.5%
0.00218 15964
 
0.5%
0.00819 15171
 
0.5%
0.0819 14603
 
0.5%
0.00983 14272
 
0.5%
0.03276 14052
 
0.5%
0.02457 13014
 
0.4%
0.01092 12684
 
0.4%
0.00491 12504
 
0.4%
Other values (101944) 2840236
95.0%
ValueCountFrequency (%)
0.0001 170
 
< 0.1%
0.00013 79
 
< 0.1%
0.00018 159
 
< 0.1%
0.0002 238
 
< 0.1%
0.00021 628
< 0.1%
0.00022 104
 
< 0.1%
0.00023 744
< 0.1%
0.00025 299
< 0.1%
0.00026 262
 
< 0.1%
0.00029 137
 
< 0.1%
ValueCountFrequency (%)
551.56137 1
< 0.1%
510.65893 1
< 0.1%
444.41192 1
< 0.1%
439.90647 1
< 0.1%
437.37767 1
< 0.1%
416.64823 1
< 0.1%
407.02225 1
< 0.1%
393.26092 1
< 0.1%
389.02653 1
< 0.1%
384.82574 1
< 0.1%

tn
Real number (ℝ)

High correlation  Skewed 

Distinct101922
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46684062
Minimum0.0001
Maximum547.87849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2025-05-19T00:03:17.474287image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.00209
Q10.01052
median0.0409
Q30.1638
95-th percentile1.58995
Maximum547.87849
Range547.87839
Interquartile range (IQR)0.15328

Descriptive statistics

Standard deviation3.1598884
Coefficient of variation (CV)6.7686664
Kurtosis2850.397
Mean0.46684062
Median Absolute Deviation (MAD)0.03653
Skewness37.875802
Sum1395223.2
Variance9.9848948
MonotonicityNot monotonic
2025-05-19T00:03:17.656871image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01638 19931
 
0.7%
0.04095 16228
 
0.5%
0.00218 15965
 
0.5%
0.00819 15181
 
0.5%
0.0819 14608
 
0.5%
0.00983 14272
 
0.5%
0.03276 14070
 
0.5%
0.02457 13013
 
0.4%
0.01092 12686
 
0.4%
0.00491 12502
 
0.4%
Other values (101912) 2840194
95.0%
ValueCountFrequency (%)
0.0001 170
 
< 0.1%
0.00013 79
 
< 0.1%
0.00018 159
 
< 0.1%
0.0002 238
 
< 0.1%
0.00021 628
< 0.1%
0.00022 104
 
< 0.1%
0.00023 746
< 0.1%
0.00025 299
< 0.1%
0.00026 262
 
< 0.1%
0.00029 137
 
< 0.1%
ValueCountFrequency (%)
547.87849 1
< 0.1%
469.45761 1
< 0.1%
439.90647 1
< 0.1%
437.37767 1
< 0.1%
430.90803 1
< 0.1%
414.05146 1
< 0.1%
389.02653 1
< 0.1%
386.60688 1
< 0.1%
384.82574 1
< 0.1%
379.4427 1
< 0.1%

cat1
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing7448
Missing (%)0.2%
Memory size169.8 MiB
PC
1657313 
HC
746562 
FOODS
571148 
REF
 
6179

Length

Max length5
Median length2
Mean length2.576822
Min length2

Characters and Unicode

Total characters7682027
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHC
2nd rowHC
3rd rowHC
4th rowHC
5th rowHC

Common Values

ValueCountFrequency (%)
PC 1657313
55.5%
HC 746562
25.0%
FOODS 571148
 
19.1%
REF 6179
 
0.2%
(Missing) 7448
 
0.2%

Length

2025-05-19T00:03:17.805538image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-19T00:03:17.893110image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
pc 1657313
55.6%
hc 746562
25.0%
foods 571148
 
19.2%
ref 6179
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 2403875
31.3%
P 1657313
21.6%
O 1142296
14.9%
H 746562
 
9.7%
F 577327
 
7.5%
D 571148
 
7.4%
S 571148
 
7.4%
R 6179
 
0.1%
E 6179
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7682027
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2403875
31.3%
P 1657313
21.6%
O 1142296
14.9%
H 746562
 
9.7%
F 577327
 
7.5%
D 571148
 
7.4%
S 571148
 
7.4%
R 6179
 
0.1%
E 6179
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7682027
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2403875
31.3%
P 1657313
21.6%
O 1142296
14.9%
H 746562
 
9.7%
F 577327
 
7.5%
D 571148
 
7.4%
S 571148
 
7.4%
R 6179
 
0.1%
E 6179
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7682027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2403875
31.3%
P 1657313
21.6%
O 1142296
14.9%
H 746562
 
9.7%
F 577327
 
7.5%
D 571148
 
7.4%
S 571148
 
7.4%
R 6179
 
0.1%
E 6179
 
0.1%

cat2
Categorical

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing7448
Missing (%)0.2%
Memory size184.4 MiB
CABELLO
813398 
DEOS
510270 
SOPAS Y CALDOS
344693 
ROPA LAVADO
266667 
HOGAR
223478 
Other values (10)
822696 

Length

Max length19
Median length14
Mean length7.6951468
Min length2

Characters and Unicode

Total characters22940787
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVAJILLA
2nd rowVAJILLA
3rd rowVAJILLA
4th rowVAJILLA
5th rowVAJILLA

Common Values

ValueCountFrequency (%)
CABELLO 813398
27.2%
DEOS 510270
17.1%
SOPAS Y CALDOS 344693
11.5%
ROPA LAVADO 266667
 
8.9%
HOGAR 223478
 
7.5%
PIEL2 209945
 
7.0%
ADEREZOS 204671
 
6.8%
VAJILLA 155239
 
5.2%
PIEL1 90819
 
3.0%
ROPA ACONDICIONADOR 82492
 
2.8%
Other values (5) 79530
 
2.7%

Length

2025-05-19T00:03:18.015210image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cabello 813398
20.2%
deos 510270
12.7%
ropa 359332
8.9%
sopas 344693
8.6%
y 344693
8.6%
caldos 344693
8.6%
lavado 266667
 
6.6%
hogar 223478
 
5.5%
piel2 209945
 
5.2%
aderezos 204671
 
5.1%
Other values (8) 408080
10.1%

Most occurring characters

ValueCountFrequency (%)
O 3375272
14.7%
A 3360801
14.6%
L 2890792
12.6%
E 2081347
9.1%
S 1789490
7.8%
D 1524166
6.6%
C 1333248
 
5.8%
1048718
 
4.6%
P 1013302
 
4.4%
R 900270
 
3.9%
Other values (14) 3623381
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21591305
94.1%
Space Separator 1048718
 
4.6%
Decimal Number 300764
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 3375272
15.6%
A 3360801
15.6%
L 2890792
13.4%
E 2081347
9.6%
S 1789490
8.3%
D 1524166
7.1%
C 1333248
 
6.2%
P 1013302
 
4.7%
R 900270
 
4.2%
B 813398
 
3.8%
Other values (11) 2509219
11.6%
Decimal Number
ValueCountFrequency (%)
2 209945
69.8%
1 90819
30.2%
Space Separator
ValueCountFrequency (%)
1048718
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21591305
94.1%
Common 1349482
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 3375272
15.6%
A 3360801
15.6%
L 2890792
13.4%
E 2081347
9.6%
S 1789490
8.3%
D 1524166
7.1%
C 1333248
 
6.2%
P 1013302
 
4.7%
R 900270
 
4.2%
B 813398
 
3.8%
Other values (11) 2509219
11.6%
Common
ValueCountFrequency (%)
1048718
77.7%
2 209945
 
15.6%
1 90819
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22940787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 3375272
14.7%
A 3360801
14.6%
L 2890792
12.6%
E 2081347
9.1%
S 1789490
7.8%
D 1524166
6.6%
C 1333248
 
5.8%
1048718
 
4.6%
P 1013302
 
4.4%
R 900270
 
3.9%
Other values (14) 3623381
15.8%

cat3
Text

Distinct93
Distinct (%)< 0.1%
Missing7448
Missing (%)0.2%
Memory size185.9 MiB
2025-05-19T00:03:18.339409image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.8008947
Min length3

Characters and Unicode

Total characters23256043
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCristalino
2nd rowCristalino
3rd rowCristalino
4th rowCristalino
5th rowCristalino
ValueCountFrequency (%)
shampoo 380777
 
10.8%
aero 337515
 
9.6%
acondicionador 308574
 
8.8%
polvo 153986
 
4.4%
liquido 126363
 
3.6%
sopas 121289
 
3.4%
jabon 110439
 
3.1%
mayonesa 108842
 
3.1%
gel 102749
 
2.9%
noaero 81470
 
2.3%
Other values (88) 1694129
48.0%
2025-05-19T00:03:18.905098image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 2097836
 
9.0%
O 1879074
 
8.1%
A 1782653
 
7.7%
a 1527350
 
6.6%
e 1131100
 
4.9%
C 992719
 
4.3%
r 987568
 
4.2%
S 848721
 
3.6%
N 791113
 
3.4%
l 789103
 
3.4%
Other values (41) 10428806
44.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11379031
48.9%
Lowercase Letter 11332081
48.7%
Space Separator 544931
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2097836
18.5%
a 1527350
13.5%
e 1131100
10.0%
r 987568
8.7%
l 789103
 
7.0%
s 741838
 
6.5%
i 662332
 
5.8%
n 564373
 
5.0%
u 431503
 
3.8%
d 397628
 
3.5%
Other values (15) 2001450
17.7%
Uppercase Letter
ValueCountFrequency (%)
O 1879074
16.5%
A 1782653
15.7%
C 992719
8.7%
S 848721
7.5%
N 791113
7.0%
I 750823
 
6.6%
D 719180
 
6.3%
P 684344
 
6.0%
M 646255
 
5.7%
R 569156
 
5.0%
Other values (15) 1714993
15.1%
Space Separator
ValueCountFrequency (%)
544931
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22711112
97.7%
Common 544931
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2097836
 
9.2%
O 1879074
 
8.3%
A 1782653
 
7.8%
a 1527350
 
6.7%
e 1131100
 
5.0%
C 992719
 
4.4%
r 987568
 
4.3%
S 848721
 
3.7%
N 791113
 
3.5%
l 789103
 
3.5%
Other values (40) 9883875
43.5%
Common
ValueCountFrequency (%)
544931
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23205927
99.8%
None 50116
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2097836
 
9.0%
O 1879074
 
8.1%
A 1782653
 
7.7%
a 1527350
 
6.6%
e 1131100
 
4.9%
C 992719
 
4.3%
r 987568
 
4.3%
S 848721
 
3.7%
N 791113
 
3.4%
l 789103
 
3.4%
Other values (40) 10378690
44.7%
None
ValueCountFrequency (%)
ñ 50116
100.0%

brand
Categorical

High correlation 

Distinct37
Distinct (%)< 0.1%
Missing7448
Missing (%)0.2%
Memory size180.5 MiB
NIVEA
384335 
SHAMPOO3
338209 
MAGGI
322839 
DEOS1
299785 
MUSCULO
242680 
Other values (32)
1393354 

Length

Max length10
Median length9
Mean length6.3284487
Min length3

Characters and Unicode

Total characters18866384
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowImportado
2nd rowImportado
3rd rowImportado
4th rowImportado
5th rowImportado

Common Values

ValueCountFrequency (%)
NIVEA 384335
12.9%
SHAMPOO3 338209
11.3%
MAGGI 322839
10.8%
DEOS1 299785
10.0%
MUSCULO 242680
 
8.1%
LIMPIEX 217199
 
7.3%
SHAMPOO2 141777
 
4.7%
NATURA 120648
 
4.0%
SHAMPOO1 109610
 
3.7%
COLBERT 89406
 
3.0%
Other values (27) 714714
23.9%

Length

2025-05-19T00:03:19.101841image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nivea 384335
12.9%
shampoo3 338209
11.3%
maggi 322839
10.8%
deos1 299785
10.1%
musculo 242680
 
8.1%
limpiex 217199
 
7.3%
shampoo2 141777
 
4.8%
natura 120648
 
4.0%
shampoo1 109610
 
3.7%
colbert 89406
 
3.0%
Other values (27) 714714
24.0%

Most occurring characters

ValueCountFrequency (%)
O 2259859
12.0%
A 2147786
 
11.4%
M 1578291
 
8.4%
I 1449134
 
7.7%
E 1399987
 
7.4%
S 1373798
 
7.3%
P 948177
 
5.0%
L 781952
 
4.1%
N 770778
 
4.1%
G 727286
 
3.9%
Other values (25) 5429336
28.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17595246
93.3%
Decimal Number 1155634
 
6.1%
Lowercase Letter 115504
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2259859
12.8%
A 2147786
12.2%
M 1578291
 
9.0%
I 1449134
 
8.2%
E 1399987
 
8.0%
S 1373798
 
7.8%
P 948177
 
5.4%
L 781952
 
4.4%
N 770778
 
4.4%
G 727286
 
4.1%
Other values (15) 4158198
23.6%
Lowercase Letter
ValueCountFrequency (%)
o 28876
25.0%
m 14438
12.5%
p 14438
12.5%
r 14438
12.5%
t 14438
12.5%
a 14438
12.5%
d 14438
12.5%
Decimal Number
ValueCountFrequency (%)
1 556839
48.2%
3 401281
34.7%
2 197514
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 17710750
93.9%
Common 1155634
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2259859
12.8%
A 2147786
12.1%
M 1578291
 
8.9%
I 1449134
 
8.2%
E 1399987
 
7.9%
S 1373798
 
7.8%
P 948177
 
5.4%
L 781952
 
4.4%
N 770778
 
4.4%
G 727286
 
4.1%
Other values (22) 4273702
24.1%
Common
ValueCountFrequency (%)
1 556839
48.2%
3 401281
34.7%
2 197514
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18866384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 2259859
12.0%
A 2147786
 
11.4%
M 1578291
 
8.4%
I 1449134
 
7.7%
E 1399987
 
7.4%
S 1373798
 
7.3%
P 948177
 
5.0%
L 781952
 
4.1%
N 770778
 
4.1%
G 727286
 
3.9%
Other values (25) 5429336
28.8%

sku_size
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)< 0.1%
Missing7448
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean445.277
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2025-05-19T00:03:19.355366image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q190
median240
Q3450
95-th percentile1000
Maximum10000
Range9999
Interquartile range (IQR)360

Descriptive statistics

Standard deviation741.1227
Coefficient of variation (CV)1.6644082
Kurtosis39.527448
Mean445.277
Median Absolute Deviation (MAD)160
Skewness5.1284545
Sum1.3274607 × 109
Variance549262.86
MonotonicityNot monotonic
2025-05-19T00:03:19.640272image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 306300
 
10.2%
400 214118
 
7.2%
350 200788
 
6.7%
90 173479
 
5.8%
50 155471
 
5.2%
10 125059
 
4.2%
750 122149
 
4.1%
100 120666
 
4.0%
300 103069
 
3.4%
800 90204
 
3.0%
Other values (65) 1369899
45.8%
ValueCountFrequency (%)
1 14556
 
0.5%
2 21958
 
0.7%
3 3010
 
0.1%
4 20122
 
0.7%
5 49197
 
1.6%
6 14872
 
0.5%
8 18309
 
0.6%
10 125059
4.2%
12 30376
 
1.0%
15 28855
 
1.0%
ValueCountFrequency (%)
10000 3052
 
0.1%
7500 32
 
< 0.1%
5000 14532
 
0.5%
4500 38
 
< 0.1%
4000 18498
 
0.6%
3000 80012
2.7%
2000 7768
 
0.3%
1800 508
 
< 0.1%
1500 12842
 
0.4%
1400 3906
 
0.1%

stock_final
Real number (ℝ)

Missing  Zeros 

Distinct12596
Distinct (%)1.1%
Missing1839319
Missing (%)61.5%
Infinite0
Infinite (%)0.0%
Mean27.139183
Minimum-27.31136
Maximum1562.0245
Zeros34082
Zeros (%)1.1%
Negative28122
Negative (%)0.9%
Memory size22.8 MiB
2025-05-19T00:03:19.898557image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-27.31136
5-th percentile0
Q11.76106
median7.17641
Q323.05673
95-th percentile111.2202
Maximum1562.0245
Range1589.3358
Interquartile range (IQR)21.29567

Descriptive statistics

Standard deviation74.750983
Coefficient of variation (CV)2.7543564
Kurtosis114.17328
Mean27.139183
Median Absolute Deviation (MAD)6.70467
Skewness8.9532795
Sum31191904
Variance5587.7094
MonotonicityNot monotonic
2025-05-19T00:03:20.210160image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34082
 
1.1%
0.049 727
 
< 0.1%
0.11394 521
 
< 0.1%
0.7204 470
 
< 0.1%
3.42342 468
 
< 0.1%
-1.57248 450
 
< 0.1%
0.04423 447
 
< 0.1%
17.26234 445
 
< 0.1%
-0.01747 440
 
< 0.1%
27.70186 432
 
< 0.1%
Other values (12586) 1110849
37.2%
(Missing) 1839319
61.5%
ValueCountFrequency (%)
-27.31136 206
< 0.1%
-13.66656 65
 
< 0.1%
-13.33127 196
< 0.1%
-8.19961 64
 
< 0.1%
-8.15986 86
 
< 0.1%
-7.7212 24
 
< 0.1%
-5.86579 65
 
< 0.1%
-5.28091 94
 
< 0.1%
-5.18307 242
< 0.1%
-5.0992 51
 
< 0.1%
ValueCountFrequency (%)
1562.02448 221
< 0.1%
1284.38214 158
< 0.1%
1212.36734 158
< 0.1%
1146.09799 213
< 0.1%
1097.55623 149
< 0.1%
1057.38804 189
< 0.1%
1037.85386 186
< 0.1%
1031.01561 176
< 0.1%
978.16446 46
 
< 0.1%
916.3419 215
< 0.1%

Interactions

2025-05-19T00:02:52.935063image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:24.443558image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:29.513243image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:34.338886image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:38.844869image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:43.217425image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:48.204666image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:53.529270image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:25.219872image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:30.240011image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:35.163590image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:39.457844image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:43.830255image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:48.963035image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:53.986064image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:25.940271image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:30.893498image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:35.848471image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:40.056763image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:44.641224image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:49.746582image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:54.401546image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:26.682564image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:31.469436image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:36.479307image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:40.845394image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:45.443498image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:50.462338image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:54.836792image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:27.310619image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:32.282200image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:37.121400image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:41.463441image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:46.406347image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:51.425576image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:55.384635image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:28.444728image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:32.988867image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:37.803328image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:42.189207image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:47.077583image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:52.166128image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:55.815459image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:28.837915image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:33.484412image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:38.205229image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:42.573088image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:47.481855image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-05-19T00:02:52.518443image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-05-19T00:03:20.439088image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
brandcat1cat2cust_request_qtycust_request_tncustomer_idplan_precios_cuidadosproduct_idsku_sizestock_finaltn
brand1.0001.0000.8340.0120.0180.0390.2280.3390.2650.1370.018
cat11.0001.0001.0000.0160.0180.0540.0410.3430.2260.1320.018
cat20.8341.0001.0000.0120.0160.0370.1200.2640.3750.1180.015
cust_request_qty0.0120.0160.0121.0000.376-0.4520.003-0.0080.009-0.0100.376
cust_request_tn0.0180.0180.0160.3761.000-0.5120.000-0.5920.4720.3241.000
customer_id0.0390.0540.037-0.452-0.5121.0000.006-0.007-0.031-0.007-0.512
plan_precios_cuidados0.2280.0410.1200.0030.0000.0061.0000.0660.0190.0120.000
product_id0.3390.3430.264-0.008-0.592-0.0070.0661.000-0.552-0.443-0.592
sku_size0.2650.2260.3750.0090.472-0.0310.019-0.5521.0000.3540.472
stock_final0.1370.1320.118-0.0100.324-0.0070.012-0.4430.3541.0000.324
tn0.0180.0180.0150.3761.000-0.5120.000-0.5920.4720.3241.000

Missing values

2025-05-19T00:02:56.626914image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-19T00:03:02.176842image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-19T00:03:12.101222image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

periodocustomer_idproduct_idplan_precios_cuidadoscust_request_qtycust_request_tntncat1cat2cat3brandsku_sizestock_final
02017-01-011023420524020.053000.05300HCVAJILLACristalinoImportado500.0NaN
12017-01-011003220524010.136280.13628HCVAJILLACristalinoImportado500.0NaN
22017-01-011021720524010.030280.03028HCVAJILLACristalinoImportado500.0NaN
32017-01-011012520524010.022710.02271HCVAJILLACristalinoImportado500.0NaN
42017-01-0110012205240111.544521.54452HCVAJILLACristalinoImportado500.0NaN
52017-01-011008020524010.015140.01514HCVAJILLACristalinoImportado500.0NaN
62017-01-011001520524040.106000.10600HCVAJILLACristalinoImportado500.0NaN
72017-01-011006220524010.189280.18928HCVAJILLACristalinoImportado500.0NaN
82017-01-011015920524030.022710.02271HCVAJILLACristalinoImportado500.0NaN
92017-01-011018320524010.015140.01514HCVAJILLACristalinoImportado500.0NaN
periodocustomer_idproduct_idplan_precios_cuidadoscust_request_qtycust_request_tntncat1cat2cat3brandsku_sizestock_final
29886402019-12-011002120853080.158290.15829PCCABELLOShampoo BebeNIVEA200.01.82373
29886412019-12-011009320853010.055740.05574PCCABELLOShampoo BebeNIVEA200.01.82373
29886422019-12-011000320853090.624260.62426PCCABELLOShampoo BebeNIVEA200.01.82373
29886432019-12-011036720853010.004460.00446PCCABELLOShampoo BebeNIVEA200.01.82373
29886442019-12-011027820853050.060200.06020PCCABELLOShampoo BebeNIVEA200.01.82373
29886452019-12-011010520853010.022300.02230PCCABELLOShampoo BebeNIVEA200.01.82373
29886462019-12-011009220853010.006690.00669PCCABELLOShampoo BebeNIVEA200.01.82373
29886472019-12-011000620853070.028980.02898PCCABELLOShampoo BebeNIVEA200.01.82373
29886482019-12-011001820853040.015610.01561PCCABELLOShampoo BebeNIVEA200.01.82373
29886492019-12-011002020853020.015610.01561PCCABELLOShampoo BebeNIVEA200.01.82373

Duplicate rows

Most frequently occurring

periodocustomer_idproduct_idplan_precios_cuidadoscust_request_qtycust_request_tntncat1cat2cat3brandsku_sizestock_final# duplicates
02017-01-011000120010031.319141.31914HCROPA LAVADOPolvoLIMPIEX400.0NaN2
12017-01-011000120021031.878241.87824HCROPA LAVADOPolvoLIMPIEX400.0NaN2
22017-01-01100012002201015.3578915.35789HCROPA LAVADOPolvoLIMPIEX800.0NaN2
32017-01-011000120037065.402785.40278FOODSSOPAS Y CALDOSCaldo CuboMAGGI12.0NaN2
42017-01-011000120105086.950366.95036FOODSSOPAS Y CALDOSSalsas WetMAGGI350.0NaN2
52017-01-01100022001001657.7711756.09386HCROPA LAVADOPolvoLIMPIEX400.0NaN2
62017-01-01100022002001229.2481329.24813HCROPA LAVADOPolvoLIMPIEX800.0NaN2
72017-01-01100022002102237.4949136.21072HCROPA LAVADOPolvoLIMPIEX400.0NaN2
82017-01-01100022002201142.0026942.00269HCROPA LAVADOPolvoLIMPIEX800.0NaN2
92017-01-01100022003702615.8099815.11284FOODSSOPAS Y CALDOSCaldo CuboMAGGI12.0NaN2